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README.md

@@ -59,10 +59,15 @@ pipeline_tag: text-generation
 
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-## 📈 Training (v1 lineage, preserved)
+## 📈 Training
+
+**Current release (v2).** In-kernel held-out PPL, steps 62000 → 80000 (bf16, 200k-token slice): 47.11 → **43.09**, decelerating into the cosine floor — the reason the next run re-warms to peak LR 6e-4 instead of adding more low-LR steps.
+
+
+### v1 lineage (frozen)
 
 <p align="center">
-  <img src="neuralai-nae1-training.png" alt="Nae1 training loss step 28000 to 32000" width="92%" />
+  <img src="neuralai-nae1-training.png" alt="Nae1 v1 training loss step 28000 to 32000" width="92%" />
 </p>
 
 Nae1 v1 was pretrained from random initialization in eight cosine cycles: 0 → 4,000 · 4,000 → 8,000 · … · 24,000 → 28,000 · 28,000 → 32,000, each resumed from the previous checkpoint. The chart shows cycle eight — the **first cycle trained with corrected next-token labels** (an earlier double-shift bug had models predicting token t+2; the held-out gate was always standard next-token, so historical PPL numbers remain valid). Under the corrected objective the logged loss resumes at 28.87, falls to a best of **15.72** (step 31,500) and finishes at **17.93** as the learning rate anneals to zero. Held-out PPL improves 147.97 → 146.17 → **54.16** — a 63% drop from fixing the training objective; a fresh-corpus cross-check (unseen FineWeb CC-MAIN-2014-10: **57.09** vs step-28000's **143.64**) confirms the jump is genuine generalization, not eval-set leakage.